An overlapping pattern of cerebral cortical thinning is associated with both positive symptoms and aggression in schizophrenia via the ENIGMA consortium

2019 ◽  
Vol 50 (12) ◽  
pp. 2034-2045 ◽  
Author(s):  
Ting Yat Wong ◽  
Joaquim Radua ◽  
Edith Pomarol-Clotet ◽  
Raymond Salvador ◽  
Anton Albajes-Eizagirre ◽  
...  
Keyword(s):  
Brain Mri ◽  
Brain Structures ◽  
Primary Diagnosis ◽  
Positive Symptoms ◽  
Formal Thought Disorder ◽  
Midcingulate Cortex ◽  
Neuroimaging Data ◽  
Mri Scans ◽  
Cortical Gray Matter ◽  
Abnormal Brain

AbstractBackgroundPositive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample.MethodTo study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls.ResultsThe result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression.ConclusionThese findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.

scholarly journals Multidimensional Heritability Analysis of Neuroanatomical Shape

2015 ◽  
Author(s):  
Tian Ge ◽  
Martin Reuter ◽  
Anderson M. Winkler ◽  
Avram J. Holmes ◽  
Phil H. Lee ◽  
...  
Keyword(s):  
Brain Structure ◽  
Large Scale ◽  
Brain Mri ◽  
Brain Structures ◽  
Biomedical Data ◽  
Genetic Determinants ◽  
Genetic Studies ◽  
Genome Wide ◽  
Human Connectome Project ◽  
Mri Scans

In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behavior and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.

scholarly journals Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI

2020 ◽  
Vol 10 (12) ◽  
pp. 974
Author(s):  
Minho Lee ◽  
JeeYoung Kim ◽  
Regina EY Kim ◽  
Hyun Gi Kim ◽  
Se Won Oh ◽  
...  
Keyword(s):  
Learning Strategy ◽  
Structural Information ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Fine Tuning ◽  
Brain Segmentation ◽  
Convolutional Network ◽  
Neuroimaging Data ◽  
Mri Scans ◽  
Split Attention

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. The proposed architecture employs split-attention blocks, skip pathways with pyramid levels, and evolving normalization layers. For efficient training, we performed pre-training and fine-tuning with the original and manually modified FreeSurfer labels, respectively. This learning strategy enables involvement of heterogeneous neuroimaging data in the training without the need for many manual annotations. Using nine evaluation datasets, we demonstrated that SAU-Net achieved better segmentation accuracy with better reliability that surpasses those of state-of-the-art methods. We believe that SAU-Net has excellent potential due to its robustness to neuroanatomical variability that would enable almost instantaneous access to accurate neuroimaging biomarkers and its swift processing runtime compared to other methods investigated.

scholarly journals Postoperative cerebral air embolism with delayed abnormal brain MRI findings

2021 ◽  
Vol 22 ◽  
pp. 100305
Author(s):  
Yuwa Oka ◽  
Koji Tsuzaki ◽  
Mayu Kamei ◽  
Akihiro Kikuya ◽  
Toshiaki Hamano
Keyword(s):  
Brain Mri ◽  
Air Embolism ◽  
Mri Findings ◽  
Cerebral Air Embolism ◽  
Abnormal Brain

Intracranial meningioma in a patient with osteogenesis imperfecta

Open Medicine ◽  
2008 ◽  
Vol 3 (4) ◽  
pp. 517-520
Author(s):  
Parmenion Tsitsopoulos ◽  
Ioannis Anagnostopoulos ◽  
Vasileios Tsitouras ◽  
Ioannis Venizelos ◽  
Philippos Tsitsopoulos
Keyword(s):  
Osteogenesis Imperfecta ◽  
Muscle Power ◽  
Brain Mri ◽  
Intracranial Meningioma ◽  
Type I ◽  
Mri Scans ◽  
Heritable Disorder ◽  
History Of ◽  
One Year ◽  
Gait Disturbances

AbstractOsteogenesis imperfecta (OI) is a heritable disorder characterized mainly by connective tissue manifestations. In dinstinct cases, several neurological features have also been described. A 46-year-old male with a known family history of OI type I presented with progressive gait disturbances and diminished muscle strength. Brain MRI scans revealed an infiltrative intracranial mass occupying both frontoparietal lobes. The patient underwent surgical intervention. The histological diagnosis was an atypical (Grade II) meningioma. The bony parts demonstrated a mixture of osseous defects due to OI and infiltration by the tumor. At one-year follow up the patient′s muscle power partially returned while repeat MRI scans were negative for tumor recurrence.

scholarly journals Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders

2020 ◽  
Author(s):  
Xin Niu ◽  
Alexei Taylor ◽  
Russell T. Shinohara ◽  
John Kounios ◽  
Fengqing Zhang
Keyword(s):  
Psychiatric Disorders ◽  
Robust Regression ◽  
Developmental Trajectories ◽  
Brain Structures ◽  
Brain Regions ◽  
Developmental Trajectory ◽  
Imaging Features ◽  
Neuroimaging Data ◽  
Brain Age ◽  
Age Prediction

AbstractBrain regions change in different ways and at different rates. This staggered developmental unfolding is determined by genetics and postnatal experience and is implicated in the progression of psychiatric and neurological disorders. Neuroimaging-based brain-age prediction has emerged as an important new approach for studying brain development. However, the unidimensional brain-age estimates provided by previous methods do not capture the divergent developmental trajectories of various brain structures. Here we propose and illustrate an analytic pipeline to compute an index of multidimensional brain-age that provides regional age predictions. First, using a database of 556 subjects that includes psychiatric and neurological patients as well as healthy controls we conducted robust regression to characterize the developmental trajectory of each MRI-based brain-imaging feature. We then utilized cluster analysis to identify subgroups of imaging features with a similar developmental trajectory. For each identified cluster, we obtained a brain-age prediction by applying machine-learning models with imaging features belonging to each cluster. Brain-age predictions from multiple clusters form a multidimensional brain-age index (MBAI). The MBAI is more sensitive to alterations in brain structures and captured distinct regional change patterns. In particular, the MBAI provided a more flexible analysis of brain age across brain regions that revealed changes in specific structures in psychiatric disorders that would otherwise have been combined in a unidimensional brain age prediction. More generally, brain-age prediction using a subset of homogeneous features circumvents the curse of dimensionality in neuroimaging data.

scholarly journals Automatic linear measurements of the fetal brain on MRI with deep neural networks

2021 ◽  
Author(s):  
Netanell Avisdris ◽  
Bossmat Yehuda ◽  
Ori Ben-Zvi ◽  
Daphna Link-Sourani ◽  
Liat Ben-Sira ◽  
...  
Keyword(s):  
Human Error ◽  
Brain Mri ◽  
Fetal Brain ◽  
Region Of Interest ◽  
Fetal Mri ◽  
Brain Structures ◽  
Automatic Method ◽  
Linear Measurements ◽  
Biparietal Diameter ◽  
Reliable Assessment

Abstract Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.Methods: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (I of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.Conclusions: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

scholarly journals Silent Brain MRI Infarcts and Subsequent Stroke Type In the Cardiovascular health Study

Stroke ◽  
2001 ◽  
Vol 32 (suppl_1) ◽  
pp. 363-363
Author(s):  
Charles B Bernick ◽  
Lewis H Kuller ◽  
Will T Longstreth ◽  
Corinne Dulberg ◽  
Teri A Manolio ◽  
...  
Keyword(s):  
Hemorrhagic Stroke ◽  
Cardiovascular Health ◽  
Brain Mri ◽  
The Elderly ◽  
Health Study ◽  
Cardiovascular Health Study ◽  
Prior History ◽  
Mri Scans ◽  
Cranial Mri ◽  
Silent Mri

P136 Objective: Silent infarcts seen on cranial MRI scans are a risk factor for subsequent clinical stroke in the elderly. This study examines the type of clinical strokes seen in those with silent infarcts. Methods: Cranial MRI examination was completed on 3324 Cardiovascular Health Study (CHS) participants aged 65+ who were without a prior history of clinical stroke. Incident strokes were identified over an average follow-up of 4 years and classified as hemorrhagic or ischemic. Ischemic strokes were further subdivided into lacunar, cardioembolic, atherosclerotic or other/unknown. Results: Silent MRI infarcts >3mm were found in approximately 28% (n=923). Of these, 7% (n=67) subsequently had a clinically evident stroke. The characteristics of the silent MRI infarcts in those who sustained an incident stroke were as folows: 56 had only subcortical infarcts, of which 55 were <20mm; 4 had only cortical infarcts; and 7 had both cortical and subcortical infarcts. Of those with only subcortical silent MRI infarcts, 16% (n=9) went on to a hemorrhagic stroke and 84% (n=47) sustained an ischemic stroke. The ischemic strokes were subtyped as 12 cardioembolic, 3 lacunar, 2 atherosclerotic and 30 unknown/other. Considering only those with cortical silent infarcts, either alone or in combination with subcortical infarcts, there was 1 hemorrhagic stroke and 10 ischemic strokes. Half of the ischemic strokes were cardioembolic and half were unknown type. Conclusion: Elderly individuals with silent subcortical infarcts who go onto subsequent stroke may be at risk not only for lacunar infarcts but also cardioembolic or hemorrhagic strokes.

scholarly journals Early infantile epileptic-dyskinetic encephalopathy due to biallelic PIGP mutations

2020 ◽  
Vol 6 (1) ◽  
pp. e387 ◽  
Author(s):  
Annalisa Vetro ◽  
Tiziana Pisano ◽  
Silvia Chiaro ◽  
Elena Procopio ◽  
Azzurra Guerra ◽  
...  
Keyword(s):  
Neurodevelopmental Disorder ◽  
Brain Mri ◽  
Molecular Genetic ◽  
Genetic Diagnosis ◽  
Premature Death ◽  
Epileptic Encephalopathy ◽  
Whole Exome ◽  
Mri Scans ◽  
Severe Hypotonia ◽  
Profound Disability

ObjectiveTo describe clinical, biochemical, and molecular genetic findings in a large inbred family in which 4 children with a severe early-onset epileptic-dyskinetic encephalopathy, with suppression burst EEG, harbored homozygous mutations of phosphatidylinositol glycan anchor biosynthesis, class P (PIGP), a member of the large glycosylphosphatidylinositol (GPI) anchor biosynthesis gene family.MethodsWe studied clinical features, EEG, brain MRI scans, whole-exome sequencing (WES), and measured the expression of a subset of GPI-anchored proteins (GPI-APs) in circulating granulocytes using flow cytometry.ResultsThe 4 affected children exhibited a severe neurodevelopmental disorder featuring severe hypotonia with early dyskinesia progressing to quadriplegia, associated with infantile spasms, focal, tonic, and tonic-clonic seizures and a burst suppression EEG pattern. Two of the children died prematurely between age 2 and 12 years; the remaining 2 children are aged 2 years 7 months and 7 years 4 months. The homozygous c.384del variant of PIGP, present in the 4 patients, introduces a frame shift 6 codons before the expected stop signal and is predicted to result in the synthesis of a protein longer than the wild type, with impaired functionality. We demonstrated a reduced expression of the GPI-AP CD16 in the granulocytic membrane in affected individuals.ConclusionsPIGP mutations are consistently associated with an epileptic-dyskinetic encephalopathy with the features of early infantile epileptic encephalopathy with profound disability and premature death. CD16 is a valuable marker to support a genetic diagnosis of inherited GPI deficiencies.

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